


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor






class      UpperCamelCase(	unittest.TestCase ):


					def __init__(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=7  ,     SCREAMING_SNAKE_CASE						:		Tuple=3  ,     SCREAMING_SNAKE_CASE						:		int=1_8  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_0  ,     SCREAMING_SNAKE_CASE						:		List[str]=4_0_0  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=True  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_2  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=True  ,     )      ->						Any:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		num_channels
											__snake_case				=		image_size
											__snake_case				=		min_resolution
											__snake_case				=		max_resolution
											__snake_case				=		do_resize
											__snake_case				=		size_divisor
											__snake_case				=		do_rescale






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Any:




											'''simple docstring'''

											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}






@require_torch
@require_vision
class      UpperCamelCase(	UpperCAmelCase__  ,				unittest.TestCase ):
					snake_case_		:					Tuple    					=						GLPNImageProcessor if is_vision_available() else None


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		GLPNImageProcessingTester(self    )


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Union[str, Any]:




											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(_a  ,     "do_resize"    )    )
											self.assertTrue(hasattr(_a  ,     "size_divisor"    )    )
											self.assertTrue(hasattr(_a  ,     "resample"    )    )
											self.assertTrue(hasattr(_a  ,     "do_rescale"    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						List[Any]:




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_a    )
											for image in image_inputs:
																	self.assertIsInstance(_a  ,     Image.Image    )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_a  ,     numpify=_a    )
											for image in image_inputs:
																	self.assertIsInstance(_a  ,     np.ndarray    )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											__snake_case				=		prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=_a  ,     torchify=_a    )
											for image in image_inputs:
																	self.assertIsInstance(_a  ,     torch.Tensor    )

											# Test not batched input (GLPNImageProcessor doesn't support batching)
											__snake_case				=		image_processing(image_inputs[0]  ,     return_tensors="pt"    ).pixel_values
											self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
											self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )











"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



